Abstract
Social bookmarking has been in the spotlight recently. Social bookmarking allows users to add several keywords called tags to items they bookmarked. Many previous works on social bookmarking using actual words for tags, called folksonomy, have come out. However, essential information of tags is in the classification of items by tags. Based on this assumption, we propose an anti-folksonomical recommendation system for calculating similarities between groups of items classified according to tags. In addition, we use hypothesis testing to improve these similarities based on statistical reliability. The experimental results show that our proposed system provides an appropriate recommendation result even if users tagged with different keywords.
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Sasaki, A., Miyata, T., Inazumi, Y., Kobayashi, A., Sakai, Y. (2010). Anti-folksonomical Recommender System for Social Bookmarking Service. In: Cordeiro, J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2009. Lecture Notes in Business Information Processing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12436-5_19
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DOI: https://doi.org/10.1007/978-3-642-12436-5_19
Publisher Name: Springer, Berlin, Heidelberg
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